An Ensemble Method for Clustering

Combination strategies in classification are a popular way of overcoming instabilities in classification algorithms. A direct application of ideas such as “voting” to cluster analysis problems is not possible, as no a priori class information for the patterns is available. We present a methodology for combining ensembles of partitions obtained by clustering, discuss the properties of such combination strategies and relate them to the task of assessing partition “aggrement”.

[1]  Ludmila I. Kuncheva,et al.  Clustering-and-selection model for classifier combination , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[2]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[3]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4]  Louisa Lam,et al.  Classifier Combinations: Implementations and Theoretical Issues , 2000, Multiple Classifier Systems.

[5]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[6]  Dave E. Eckhardt,et al.  A theoretical investigation of generalized voters for redundant systems , 1989, [1989] The Nineteenth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[7]  Ching Y. Suen,et al.  Optimal combinations of pattern classifiers , 1995, Pattern Recognition Letters.

[8]  Kwang Bo Cho,et al.  Radial basis function based adaptive fuzzy systems , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[9]  Kurt Hornik,et al.  A Combination Scheme for Fuzzy Clustering , 2002, AFSS.

[10]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[11]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[12]  Ana L. N. Fred,et al.  Finding Consistent Clusters in Data Partitions , 2001, Multiple Classifier Systems.

[13]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[14]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[15]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[17]  J. Kacprzyk.  Fuzzy-Set-Theoretic Approach to the Optimal Assignment of Work Places , 1976 .

[18]  B. Parhami.  Voting algorithms , 1994 .

[19]  P. Gader,et al.  Advances in fuzzy integration for pattern recognition , 1994, CVPR 1994.

[20]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.